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By adopting machine learning approaches, the researcher can simply take all available data into account without repeatedly thinking about which experiments are most valuable. The machine learning model understands the formulation landscape better as the number of experiments increases.

Experimental design can be used to guide experiments to find the best answer in the shortest number of steps. The advent of machine learning approaches has enabled innovative companies to augment their design of experiments with a more guided approach to not only find the ‘answer’ the quickest but also identify experiments to best improve the underlying model leading to a continual cycle of improved operational performance. At Intellegens, we use machine learning to disrupt this methodology, resulting in significant savings in time and money in the product development lifecycle.

Traditional R&D is limited by the human inability to interpret high-dimensional data and make unbiased decisions. Experiments and computational modelling can consume vast quantities of time and resources. The development of new methodologies that accelerate the discovery and design of new formulations is therefore crucial for achieving time efficiency and cost reductions. The design of new formulations is being largely carried out through traditional experimentation and intuition. The limitations attached to such processes include cost, manual effort and long times.